Abstract Short-term memory (STM) is a core cognitive function, critical for reasoning, decision-making, and flexible behavior. Though it has been recognized as a key function of interest for many decades, the critical neural substrate of STM is little understood. Recording experiments have found distributed persistent activity related to STM across multiple brain areas, yet it is unclear what the critical circuit nodes are, whether STM is supported by a single distributed circuit or involves many distinct parallel representations at the same time, and what if any are the causal relations between STM related activity in different brain areas. This gap in knowledge stems from lacks of a holistic view of the neural dynamics giving rise to STM. Comprehensive studies that examine multiple brain regions within a single STM behavior are severely lacking. Moreover, recordings alone cannot assess how signals across multiple brain regions are related to each other and to behavior. Transient perturbation is a powerful approach to probe recurrent neural networks. The response of neural dynamics following a transient perturbation are related to the network structure. The objective of this proposal is to use region-specific and temporally-precise perturbations in multiple brain regions to probe critical circuit nodes for STM. This will demonstrate the potential of our approach for identifying multi-regional circuits driving behavior and generate hypotheses regarding their network structure. We focus on a frontal cortical region (ALM) that is critical for a STM behavior we developed. Mice make a sensory discrimination, followed by a delay epoch in which they maintain a STM of their upcoming motor response. ALM neurons exhibit persistent activity that are causally related to the upcoming movement. STM neural dynamics are funneled through ALM projections to the brainstem to trigger a behavioral response. These properties make ALM an ideal entry point to read out the dynamics arising from the interactions of different circuit nodes and relate the dynamics to behavior. We will first obtain a comprehensive dataset where we transiently perturb all of the candidate brain regions for STM (identified using recordings and anatomy) while monitoring their consequences on STM dynamics in ALM with population recordings using the latest silicon probe technology (Aim1). We will then perform theoretical analysis of the perturbed dynamics leading to model classes that relate specific dynamics to behavior and serve as hypotheses for the underlying circuit structure (Aim2). If successful, this approach will identify the critical anatomical substrate and the relevant STM neural dynamics in a single behavior. The model outcome will subsequently guide a truly targeted yet comprehensive investigation of the multi-regional STM circuit.